This homework is due September 5th 6pm.

(This set of exercise is mostly taken from R for Data Science by Garrett Grolemund and Hadley Wickham.)

Exercise 1

What’s gone wrong with this code? Why are the points not blue?

library(ggplot2)
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

Answer: aes cannot interpret “blue”. if change “blue” to 1, or exclude “blue” from aes(), the points will be blue.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = 1))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

Answer:

Categorical variables:

manufacturer
model
trans
drv
fl
class

Continuous variables:

displ
cyl
year
cty
hwy

When run mpg, there are the types of variables like <chr>, <dbl>, <int> right under the name of variables. Categorical variables have a class of <chr>.

mpg

## # A tibble: 234 x 11
##    manufacturer model displ  year   cyl trans drv     cty   hwy fl    cla…
##    <chr>        <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <ch>
##  1 audi         a4      1.8  1999     4 auto… f        18    29 p     com…
##  2 audi         a4      1.8  1999     4 manu… f        21    29 p     com…
##  3 audi         a4      2    2008     4 manu… f        20    31 p     com…
##  4 audi         a4      2    2008     4 auto… f        21    30 p     com…
##  5 audi         a4      2.8  1999     6 auto… f        16    26 p     com…
##  6 audi         a4      2.8  1999     6 manu… f        18    26 p     com…
##  7 audi         a4      3.1  2008     6 auto… f        18    27 p     com…
##  8 audi         a4 q…   1.8  1999     4 manu… 4        18    26 p     com…
##  9 audi         a4 q…   1.8  1999     4 auto… 4        16    25 p     com…
## 10 audi         a4 q…   2    2008     4 manu… 4        20    28 p     com…
## # ... with 224 more rows

Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

Answer:

The continuous variable has scale from light color to dark color to different value of the variables, while categorical variable uses discrete colors to represent different value.

Using the continuous variable, year, as color.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = year))
```
Using the continuous variable, cty, as size.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = cty))
```
Using the continuous variable, cyl, as shape will cause the error:

Error: A continuous variable can not be mapped to shape.

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, shape = year))

Using the categorical variable, class, as color.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))
```
What happens if you map the same variable to multiple aesthetics?

Answer: displ is mapped to both location on the x-axis and color. The plot can be drawn successfully, however it is meaningless. The two aesthetics convey the same information. I think we only need to keep one of it.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = displ))
```
What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

Answer: Using stroke, we can change the thickness of the border for shapes (21-25). We also can set differnet color to the inside and outside of pionts.
```
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, size = cty), stroke = 1, shape = 21, color = "white", fill = "pink")
```
What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

Answer: It will create a temporary variable which represent the results of the expression. In this case, the temporary variable is a categorical variables as the color of them are discrete. TRUE means the displ of blue points are all smaller than 5. FALSE means other points that displ >= 5 with red color.
```
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))
```

Exercise 2

What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = drv, y = cyl))
```
Answer:

Empty cells means that there no values of (drv, cyl) in the cells.

In the above plot, places with no points are the same as empty cells in facet_grid(drv ~ cyl).

What plots does the following code make? What does . do?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

Answer: The symbol . ignores that dimension for faceting.

The first plot facets by drv on y-axis. And ignoring faceting x-axis.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

The second plot facets by cyl on x-axis. And ignoring faceting y-axis.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

Take the first faceted plot in this section:
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)
```
What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

Answer:

Firstly, let’s see how does the plot using color aesthetic look like:
```
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))
```
Comparing them, in my opinion:

Advantages of using faceting instead of the colour aesthetic

Using faceting, people could discover more distinct categories clearer. For example, it is very hard for me to find the point in midsize class with highest or lowest hwy value in the plot using color aesthetic. The reason is that the colors of midsize and minivan are very similar and aggregate togetehr. In this condition, if I use faceting, things will be much simpler.

Disadvantages of using faceting instead of the colour aesthetic

If people want to compare the points with each other but those points are in different class, using faceting will make things very difficult. In this condition, color aesthetic can help people distinguish the class of different points and meanwhile compare the positions of them in the same axis.

If dataset grows larger:

The advantages of faceting will be more obvious. We can imagine what will happen if there are too many points in one plot. Many of points will overlap with each other. If using faceting, it will be clearer.

Exercise 3

What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

Answer:
- line chart: geom_line()
- boxplot: geom_boxplot()
- histogram: geom_hist()
- area chart: geom_area()
Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.
```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)
```
Answer: This will draw a scatter plot that using displ as x-axis, hwy as y-axis, and colored by drv. The geom_smooth() here means separating the points into different lines based on their drv value. se = FALSE means no standard error on the lines.
```
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)
```
```
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
```
What does show.legend = FALSE do? What happens if you remove it?
Why do you think I used it earlier in the chapter?

Answer: show.legend = FALSE means do not show the legend box. In this code, without show legend, there is a legend.

For example, in the following plot, the there are lengends of car classes.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))
```
If add the show.legend = FALSE, the class legends will be hidden.
```
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), show.legend = FALSE)
```

What does the se argument to geom_smooth() do?

Answer: se means add or don’t add the standard error bands to lines.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = TRUE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Will these two graphs look different? Why/why not?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

Answer: No. Because the data and mapping of geom_point() and geom_smooth() in the second plot are the same as those in ggplot() in the first plot. geom_point() and geom_smooth() in the first plot will inherit from ggplot() object, so will be the same as the second plot.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Recreate the R code necessary to generate the following graphs.

Smooth lines for each drv

Answer:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = drv)) +
  geom_smooth(aes(linetype = drv), se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

A single smooth line, transparency by year

Answer:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = drv, alpha = year)) +
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Layered dots and an additional text information

Adding texts was not covered in class, but give it a try!

Answer:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(size = 4, color = "white") +
  geom_point(aes(colour = drv)) +
  geom_text(x=2.5, y=44, label = "Max: hwy = 44")

Exercise 4

What is the default geom associated with stat_summary()? How could you rewrite the previous plot to use that geom function instead of the stat function?

Answer: The default geom associated with stat_summary() is geom_pointrange().

Using geom_pointrange(stat = "summary").
```
ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary",
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )
```
What does geom_col() do? How is it different to geom_bar()?

Answer:

geom_bar uses stat_count as default: it counts the number of cases at each x position.

geom_col uses stat_identity as default: it leaves the data as is.

So, the height of bars using geom_bar is proportional to the number of cases in each group while geom_col() directly using the heights of bars to represent values in the data.
What variables does stat_smooth() compute? What parameters control its behaviour?

Answer:

stat_smooth() calculates:
- y: predicted value
- ymin: lower value of the confidence interval
- ymax: upper value of the confidence interval
- se: standard error
Paramaters that can control its behavior:
- method: The method used to calculate the confidence interval
- geom: The geometric object to use display the data
- position: The position adjustment to use for overlappling points on this layer, etc.

Exercise 5

What is the problem with this plot? How could you improve it?
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point()
```
Answer: The values of cty and hwy are rounded so the points appear on a grid and many points overlap each other. To solve the overplotting, I’ll use position = "jitter".
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point(position = "jitter")
```
What parameters to geom_jitter() control the amount of jittering?
Answer:
- width: controls the amount of vertical displacement,
- height: controls the amount of horizontal displacement.
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(width = 10, height = 20)
```
Compare and contrast geom_jitter() with geom_count().

Answer: geom_jitter() adds random noise to the locations points while geom_count() resizes the points according to the number of points at the locations. They are both useful in solving overplotting, however, it’s hard to say which one is better than another. Depending on differnet cases, the effectiveness will differ.

See plot using geom_jitter():
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_jitter()
```
See plot using geom_count():
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count()
```

Exercise 6

Turn a stacked bar chart into a pie chart using coord_polar().

Answer:

The stack bar chart of the class of mpg.

ggplot(mpg, aes(x = " Car Class", fill = class)) +
  geom_bar()

Using coord_polar() to turn to pie chart.

ggplot(mpg, aes(x = "Car Class", fill = class)) +
  geom_bar() +
  coord_polar(theta = "y")

What does labs() do? Read the documentation.

Answer: labs() could be used to set axis and legend labels in the individual scales and title, subtitle of the plot.
What does the plot below tell you about the relationship between city and highway mpg? Why is coord_fixed() important? What does geom_abline() do?
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline() +
  coord_fixed()
```
Answer:

The plot shows that the highway miles per gallon is generally higher than city miles per gallon.

coord_fixed() ensures the ratio of the line produced by geom_abline() is 1 (the default). Through this people can observe the plot easier.

Without coord_fixed(), the angle of geom line will not be 45 degree. Look at the following plot.
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline()
```
geom_abline() adds a reference line to the plot.

Without it, there will be no reference line in the plot. See the following plot:
```
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  coord_fixed()
```

BIOS 611 HW1 Data visualization (Chapter 3)

Tianlin Lan

09/05/2018

Exercise 1

Exercise 2

Exercise 3

Exercise 4

Exercise 5

Exercise 6